5 hours ago
Singapore, Singapore or San Francisco, CA, USAMid Level
Responsibilities
- Build systems that detect PII, quasi-identifiers, credentials, and other sensitive information and transform it according to data type and downstream use case.
- Develop and benchmark detection methods using rules, statistical models, classifiers, and LLM-based approaches.
- Build production pipelines that anonymize raw data before downstream processing, training, evaluation, or synthetic data generation.
- Create evaluation frameworks for privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts.
- Design robust systems for new data sources, schema drift, unusual formats, and sensitive information in unexpected fields.
- Translate privacy requirements into technical policies and safeguards in collaboration with engineering, research, operations, and customers.
Requirements
- Strong proficiency in Python and experience building reliable production data or ML systems.
- Experience with information extraction, named-entity recognition, classification, or related techniques for detecting rare or sensitive content.
- Ability to compare approaches across recall, precision, latency, cost, and downstream data utility.
- Understanding of redaction, masking, pseudonymization, anonymization, and synthetic data and when each is appropriate.
- Ability to reason about subtle leakage paths, edge cases, and adversarial failure modes.
- Experience building data-processing pipelines end-to-end without a fully prescribed roadmap.
- Preferred: hands-on experience with differential privacy, k-anonymity, secure aggregation, format-preserving encryption, or other privacy-enhancing technologies.
- Preferred: experience with sensitive healthcare, finance, or security data; low-latency or high-throughput ML inference; unstructured problem spaces; or early-stage startups.
Benefits
- Full-time employment with offices in San Francisco and Singapore, with remote work available for candidates able to overlap 70–80% with either time zone.
- Relocation and visa support for strong full-time candidates moving to the US or Singapore.
- Competitive compensation.
- 100% covered medical, dental, and vision insurance through Blue Shield of CA for US employees.
- Lunch and dinner for in-office employees.
- Company-wide holiday break from Christmas Eve through New Year’s Day, in addition to PTO and paid holidays.
- Equinox membership, 401(k), and commuter benefits for US employees.
- Unlimited access to tokens for ChatGPT, Claude Code, Cursor, and similar tools.
- Applications are rolling; the process includes two technical interviews and a 2–3 day work trial.
About HUD
HUD builds infrastructure for generating reinforcement-learning training data and evaluations for frontier AI agents, plus a marketplace where labs can buy and sell these assets. Its platform is used by frontier labs, Fortune 500 companies, and startups to create RL environments and post‑training datasets for RFT. Founded in 2025 and based in San Francisco, the company is privately held and a Y Combinator W25 alum.